Challenges of Integrating A Priori Information Efficiently in the Discovery of Spatio-Temporal Objects in Large Databases

TL;DR

This paper presents a flexible, modular framework for efficiently integrating a priori knowledge into the discovery of spatio-temporal objects in large databases, demonstrated with biological data.

Contribution

It introduces a modular, adaptable framework that allows flexible incorporation of diverse prior knowledge in spatio-temporal data analysis.

Findings

Framework effectively integrates heterogeneous movement behaviors.

Modular design allows adaptability to various applications.

Demonstrated with real biological object data.

Abstract

Using the knowledge discovery framework, it is possible to explore object databases and extract groups of objects with highly heterogeneous movement behavior by efficiently integrating a priori knowledge through interacting with the framework. The whole process is modular expandable and is therefore adaptive to any problem formulation. Further, the flexible use of different information allocation processes reveal a great potential to efficiently incorporate the a priori knowledge of different users in different ways. Therefore, the stepwise knowledge discovery process embedded in the knowledge discovery framework is described in detail to point out the flexibility of such a system incorporating object databases from different applications. The described framework can be used to gain knowledge out of object databases in many different fields. This knowledge can be used to gain further insights and improve the understanding of underlying phenomena. The functionality of the proposed framework is exemplarily demonstrated using a benchmark database based on real biological object data.